QUIET-SR: Quantum Image Enhancement Transformer for Single Image Super-Resolution

• URL: http://arxiv.org/abs/2503.08759v1
• Date: Tue, 11 Mar 2025 16:06:16 GMT
• Title: QUIET-SR: Quantum Image Enhancement Transformer for Single Image Super-Resolution
• Authors: Siddhant Dutta, Nouhaila Innan, Khadijeh Najafi, Sadok Ben Yahia, Muhammad Shafique,
• Abstract summary: We propose the Quantum Image Enhancement Transformer for Super-Resolution (QUIET-SR), a hybrid framework that extends the Swin transformer architecture with a novel shifted quantum window attention mechanism.<n>We evaluate our framework in MNIST (30.24 PSNR, 0.989 SSIM), FashionMNIST (29.76 PSNR, 0.976 SSIM) and the MedMNIST dataset collection, demonstrating that QUIET-SR achieves PSNR and SSIM scores comparable to state-of-the-art methods.
• Score: 5.713063730561454
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advancements in Single-Image Super-Resolution (SISR) using deep learning have significantly improved image restoration quality. However, the high computational cost of processing high-resolution images due to the large number of parameters in classical models, along with the scalability challenges of quantum algorithms for image processing, remains a major obstacle. In this paper, we propose the Quantum Image Enhancement Transformer for Super-Resolution (QUIET-SR), a hybrid framework that extends the Swin transformer architecture with a novel shifted quantum window attention mechanism, built upon variational quantum neural networks. QUIET-SR effectively captures complex residual mappings between low-resolution and high-resolution images, leveraging quantum attention mechanisms to enhance feature extraction and image restoration while requiring a minimal number of qubits, making it suitable for the Noisy Intermediate-Scale Quantum (NISQ) era. We evaluate our framework in MNIST (30.24 PSNR, 0.989 SSIM), FashionMNIST (29.76 PSNR, 0.976 SSIM) and the MedMNIST dataset collection, demonstrating that QUIET-SR achieves PSNR and SSIM scores comparable to state-of-the-art methods while using fewer parameters. These findings highlight the potential of scalable variational quantum machine learning models for SISR, marking a step toward practical quantum-enhanced image super-resolution.

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